The article deals with adaptive projective synchronization between two different chaotic systems with parametric uncertainties and external disturbances. Based on Lyapunov stability theory, the projective synchronization between a pair of different chaotic systems with fully unknown parameters are derived. An adaptive control law and a parameter update rule for uncertain parameters are designed such that the chaotic response system controls the chaotic drive system. Numerical simulation results are performed to explain the effectiveness and feasibility of the techniques.

This paper presents the synchronization between a pair of identical susceptible–infected–recovered (SIR) epidemic chaotic systems and fractional-order time derivative using active control method. The fractional derivative is described in Caputo sense. Numerical simulation results show that the method is effective and reliable for synchronizing the fractional-order chaotic systems while it allows the system to remain in chaotic state. The striking features of this paper are: the successful presentation of the stability of the equilibrium state and the revelation that time for synchronization varies with the variation in fractional-order derivatives close to the standard one for different specified values of the parameters of the system.

In this paper, we have discussed the local stability of the Mathieu–van der Pol hyperchaotic system with the fractional-order derivative. The fractional Routh–Hurwitz stability conditions were provided and were used to discuss the stability. Feedback control method was used to control chaos in the Mathieu–van der Pol system with fractional-order derivative and after controlling the chaotic behaviour of the system the synchronization between the fractional-order hyperchaotic Mathieu–van der Pol system and controlled system was introduced. In this study, modified adaptive control methods with uncertain parameters at various equilibrium points were used. Also the analysis of control time with respect to different fractional-order derivatives is the key feature of this paper. Numerical simulation results achieved using Adams–Boshforth–Moulton method show that the method is effective and reliable.